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Record W2792350383 · doi:10.1080/02786826.2018.1430350

Measuring ultrafine aerosols by direct photoionization and charge capture in continuous flow

2018· article· en· W2792350383 on OpenAlexfundno aff
Robert T. Nishida, Adam Boies, Simone Hochgreb

Bibliographic record

VenueAerosol Science and Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCambridge TrustAlphasense
KeywordsPhotoionizationAerosolElectric fieldIonParticle (ecology)Range (aeronautics)Atomic physicsCharged particleUltravioletElectrical mobilityChemistryComputational physicsMechanicsAnalytical Chemistry (journal)IonizationMaterials sciencePhysicsOptoelectronics

Abstract

fetched live from OpenAlex

Direct ultraviolet (UV) photoionization enables electrical charging of aerosol nanoparticles without relying on the collision of particles and ions. In this work, a low-strength electric field is applied during particle photoionization to capture charge as it is photoemitted from the particles in continuous flow, yielding a novel electrical current measurement. As in conventional photocharging-based measurement devices, a distinct electrical current from the remaining photocharged particles is also measured downstream. The two distinct measured currents are proportional to the total photoelectrically active area of the particles. A three-dimensional numerical model for particle and ion (dis)charging and transport is evaluated by comparing simulations of integrated electric currents with those from charged soot particles and ions in an experimental photoionization chamber. The model and experiment show good quantitative agreement for a single empirical constant, KcI, over a range of particle sizes and concentrations providing confidence in the theoretical equations and numerical method used. Copyright © 2018 American Association for Aerosol Research

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.195
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2018
Admission routes1
Has abstractyes

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